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  Modelling long-term industry energy demand and CO2 emissions in the system context using REMIND (version 3.1.0)

Pehl, M., Schreyer, F., Luderer, G. (2024): Modelling long-term industry energy demand and CO2 emissions in the system context using REMIND (version 3.1.0). - Geoscientific Model Development, 17, 5, 2015-2038.
https://doi.org/10.5194/gmd-17-2015-2024

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https://doi.org/10.5281/zenodo.10495588 (Supplementary material)
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The mrremind packages contains data preprocessing for the REMIND model.
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https://doi.org/10.5281/zenodo.8144227 (Supplementary material)
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 Creators:
Pehl, Michaja1, Author              
Schreyer, Felix1, Author              
Luderer, Gunnar1, Author              
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: This paper presents an extension of industry modelling within the REMIND integrated assessment model to industry subsectors and a projection of future industry subsector activity and energy demand for different baseline scenarios for use with the REMIND model. The industry sector is the largest greenhouse-gas-emitting energy demand sector and is considered a mitigation bottleneck. At the same time, industry subsectors are heterogeneous and face distinct emission mitigation challenges. By extending the multi-region, general equilibrium integrated assessment model REMIND to an explicit representation of four industry subsectors (cement, chemicals, steel, and other industry production), along with subsector-specific carbon capture and sequestration (CCS), we are able to investigate industry emission mitigation strategies in the context of the entire energy–economy–climate system, covering mitigation options ranging from reduced demand for industrial goods, fuel switching, and electrification to endogenous energy efficiency increases and carbon capture. We also present the derivation of both activity and final energy demand trajectories for the industry subsectors for use with the REMIND model in baseline scenarios, based on short-term continuation of historic trends and long-term global convergence. The system allows for selective variation of specific subsector activity and final energy demand across scenarios and regions to create consistent scenarios for a wide range of socioeconomic drivers and scenario story lines, like the Shared Socioeconomic Pathways (SSPs).

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Language(s): eng - English
 Dates: 2023-07-142024-01-122024-03-072024-03-07
 Publication Status: Finally published
 Pages: 24
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.5194/gmd-17-2015-2024
MDB-ID: No MDB - stored outside PIK (see DOI)
Organisational keyword: RD3 - Transformation Pathways
PIKDOMAIN: RD3 - Transformation Pathways
Working Group: Energy Systems
Model / method: REMIND
Regional keyword: Global
Research topic keyword: Energy
Research topic keyword: Decarbonization
Research topic keyword: Economics
OATYPE: Gold Open Access
 Degree: -

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Project name : ARIADNE
Grant ID : 03SFK5A
Funding program : -
Funding organization : Bundesministerium für Bildung und Forschung (BMBF)
Project name : ECEMF
Grant ID : 101022622
Funding program : Horizon 2020 (H2020)
Funding organization : European Commission (EC)

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Title: Geoscientific Model Development
Source Genre: Journal, SCI, Scopus, p3, oa
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Publ. Info: -
Pages: - Volume / Issue: 17 (5) Sequence Number: - Start / End Page: 2015 - 2038 Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals185
Publisher: Copernicus